diff --git a/litellm/llms/huggingface_restapi.py b/litellm/llms/huggingface_restapi.py
index 1160e6d8dc..43c293a7b4 100644
--- a/litellm/llms/huggingface_restapi.py
+++ b/litellm/llms/huggingface_restapi.py
@@ -102,6 +102,30 @@ def completion(
                 "stream": True if "stream" in optional_params and optional_params["stream"] == True else False,
             }
         input_text = prompt
+    elif task == "other":
+        print("task=other, custom api base")
+        if model in custom_prompt_dict:
+            # check if the model has a registered custom prompt
+            model_prompt_details = custom_prompt_dict[model]
+            prompt = custom_prompt(
+                role_dict=model_prompt_details["roles"], 
+                initial_prompt_value=model_prompt_details["initial_prompt_value"],  
+                final_prompt_value=model_prompt_details["final_prompt_value"], 
+                messages=messages
+            )
+        else:
+            prompt = prompt_factory(model=model, messages=messages)
+        inference_params = copy.deepcopy(optional_params)
+        inference_params.pop("details")
+        inference_params.pop("return_full_text")
+        print("inf params")
+        print(inference_params)
+        data = {
+            "inputs": prompt,
+            "parameters": inference_params,
+            "stream": True if "stream" in optional_params and optional_params["stream"] == True else False,
+        }
+        input_text = prompt
     ## LOGGING
     logging_obj.pre_call(
             input=input_text,
@@ -161,6 +185,8 @@ def completion(
                     for token in completion_response[0]["details"]["tokens"]:
                         sum_logprob += token["logprob"]
                     model_response["choices"][0]["message"]["logprobs"] = sum_logprob
+            elif task == "other":
+                model_response["choices"][0]["message"]["content"] = str(completion_response[0]["generated_text"])
         ## CALCULATING USAGE
         prompt_tokens = len(
             encoding.encode(input_text)